The present disclosure relates to a method for determining a vehicle pose, predicting a pose (xk, yk, θk) of vehicle on a map based on sensor data acquired by a vehicle localization system, transforming a set of map road references of a segment of a digital map from a global coordinate system to an image-frame coordinate system of a vehicle-mounted camera based on map data and predicted pose of the vehicle. The transformed set of map road references form a set of polylines in image-frame coordinate system. Identifying a set of corresponding image road reference features in an image acquired by vehicle mounted camera, where each identified road references feature defines a set of measurement coordinates (xi, yi) in image-frame. Projecting each of identified set of image road reference features onto formed set of polylines in order to obtain a set of projection points.
Legal claims defining the scope of protection, as filed with the USPTO.
3. The method according to claim 2, wherein the calibration parameters include a set of camera extrinsic parameters and a set of camera intrinsic parameters.
4. The method according to claim 1, wherein the step of predicting a pose of the vehicle comprises predicting a pose of the vehicle on a map based on sensor data acquired by the vehicle localization system and a predefined vehicle motion model.
9. The method according to claim 8, wherein the perturbing an estimated current vehicle pose and propagating the perturbed vehicle poses is based on prediction and measurement models of a Cubature Kalman Filter, and wherein the perturbed vehicle poses correspond to cubature points.
11. A non-transitory computer-readable storage medium storing one or more instructions configured to be executed by one or more processors of a vehicle localization module, the one or more instructions for performing the method according to claim 1.
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December 27, 2021
September 10, 2024
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